mlsanityData to 14 Oct 2026

Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects

arXiv 2306.05832 ↗First posted 9 Jun 2023cs.CV cs.GRcited by 2

Not on the map. Why?

Abstract (arXiv)

We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and structurally. Beautifying a sketch is challenging because of its highly abstract and heavily diverse drawing manner. Existing methods are usually confined to the distribution of their limited training samples and thus cannot beautify freely drawn sketches with rich variations. To address this challenge, we adopt a divide-and-combine strategy. Specifically, we first parse an input sketch into semantic components, beautify individual components by a learned part beautification module based on part-level implicit manifolds, and then reassemble the beautified components through a structure beautification module. With this strategy, our method can go beyond the training samples and handle novel freehand sketches. We demonstrate the effectiveness of our system with extensive experiments and a perceptive study.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 72 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

24 of 72 references.

References · 24

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  6. 2015Spatial Transformer Networkscited by 1,429
  7. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  8. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  9. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 526
  10. 2017A Neural Representation of Sketch Drawingscited by 313
  11. 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 206
  12. 2019PQ-NET: A Generative Part Seq2Seq Network for 3D Shapescited by 119
12 more
  1. 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
  2. 2019SDM-NET: Deep Generative Network for Structured Deformable Meshcited by 113
  3. 2020Sketchformer: Transformer-based Representation for Sketched Structurecited by 78
  4. 20173D Shape Reconstruction from Sketches via Multi-view Convolutional Networkscited by 45
  5. 2019TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translationcited by 40
  6. 2021Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwritingcited by 38
  7. 20173D Sketching using Multi-View Deep Volumetric Predictioncited by 37
  8. 2018SketchMate: Deep Hashing for Million-Scale Human Sketch Retrievalcited by 35
  9. 2022Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrievalcited by 27
  10. 2018Fast Sketch Segmentation and Labeling with Deep Learningcited by 9
  11. 2018Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrievalcited by 7
  12. 2020SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondencecited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.